1. The Field of the Invention
This invention relates to a decision-support system where information is analyzed to provide an individual with one or more suggested recommendations. More specifically, the present invention relates to a decision-supporting system that provides recommendations to a clinician in a standardized and reproducible form.
2. The Relevant Technology
The U.S. health care delivery system has undergone breathtaking changes since the late 1980's. Today's medical marketplace is characterized by escalating costs, diminishing resources, demands for accountability, inescapable conflicts regarding meaningful outcomes measures, and an expanding medical knowledge base. Health care, in general, is an information intensive industry where clinicians and health care providers analyze and digest an ever-increasing knowledge base of health care practices and procedures. Clinicians and health care providers use these practices and procedures to give appropriate medical care for each patient that seeks medical care.
Increasingly clinicians and health care researchers experience demands for more accurate and accessible information. The complexity of health care, its burgeoning information base, and the turbulence of the medical marketplace contribute to a medical system that grapples to efficiently synthesize and disseminate information, standardize care, and to continue to create and innovate. The obstacles to these goals are the same regardless of whether the health care delivery provider is a small hospital, long-term/skilled nursing facility, medical clinic, home health agency, hospice, emergent care unit, or large institution. All providers are faced with the need to identify solutions to manage information and make better decisions, whether those decisions are medical or business-related in nature.
Clinical decisions are of particular interest since they often influence the balance of human suffering and well-being. Clinical decisions are typically based upon the evidence-base of medicine, patient-physician factors and interactions, and external and internal constraints. Whether clinicians are serving individual patients or populations, they have always sought to base their decisions on the best available evidence or knowledge. The rapid expansion of the scientific and clinical evidence has changed the health care landscape so that no longer is the question how much of medical practice is based in evidence, but rather how much of the available evidence is applied at the front lines of patient care.
Clinicians and health care providers are acutely aware of the issues associated with practicing the available evidence at the front lines. Many attempts have been made to provide information to a clinician in a meaningful manner that supports the clinician's decision-making process. One current trend is to utilize artificial intelligence (AI) technologies to meet information management and decision-supporting needs. AI technologies or expert systems attempt to simulate the decision-support process that is easily accomplished by the human brain. The expert system typically includes a knowledge base that stores data representative of the currently available knowledge within a particular field of endeavor. An inference engine and associated “rules” or statements that control how the expert system reacts to a particular situations work with the knowledge base to generate solutions to problems posed to the expert system, such as the dose of a drug that a patient is to receive.
Various types of expert system have been developed in the medical field. For example, one type of expert system aids a physician with treating physical trauma. The expert system gathers patient data, such as the patient's height, weight, age, and sex, while collecting information related to the physical trauma. As the data is collected, the expert system generates a working file that is specific to the patient and the particular injury. This working file with a knowledge base of physical trauma and orthopedic fractures is used by the expert system to assist the clinician in treating the patient's physical trauma. Unfortunately, each working file is specific to the particular patient and the specific injury. Hence, each time the expert system is used, a new working file is generated, including the need to ask for patient data, patient history, and the like.
Another type of expert system guides a clinician with the administration and selection of therapeutic drugs and associated treatment regimens for a known disease. The expert system utilizes information gathered from a patient physical examination with a knowledge base to generate suggested treatment regimens for a known disease or medical condition. Although this type of expert system allows a clinician and a patient to generate treatment regimens together for a known disease, the expert system is limited to only those known diseases identified by the clinician. Additionally, initial generation of patient data is time consuming and cumbersome.
Therefore, there is a need for an expert system that allows for an evaluation of a patient over an extended period without the need to re-input patient data each time a clinician examines the same patient. Additionally, there is a need for a system that effectively gathers patient data without the clinician spending a long period examining the patient and evaluates the data to identify known or unknown medical conditions.